Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Anisha, C.D.; * | Arulanand, N.
Affiliations: Department of Computer Science and Engineering, PSG College of Technology, Peelamedu, Coimbatore, India
Correspondence: [*] Corresponding author. C.D. Anisha, Department of Computer Science and Engineering, PSG College of Technology, Peelamedu, Coimbatore, India. E-mail: ani.c.dass@gmail.com.
Abstract: The Spiral Drawing Test (SDT) has become a prominent clinical marker for the early diagnosis of Parkinson’s Disorder (PD) by capturing tremor symptoms. The integration of AI algorithms into a PD diagnosis system has proven to be a breakthrough objective assessment that aids professionals in decision-making. However, there is a need for improvisation of the workflow architectures of AI models to optimize the diagnosis system by reducing the misdiagnosis rate. The proposed system presents PD prediction using a Spiral Drawing Test (SDT) image modality integrated with an Artificial Intelligence (AI) algorithm. The proposed study presents three hybrid workflow architectures formed by integrating three core layers: a data augmentation layer, Transfer Layer (TL)-based feature extraction layer, and Deep Learning (DL)-based classification layer. The results were analyzed by conducting 18 experiments based on the hyperparameter values and workflow architectures. The highest accuracy obtained by the proposed study is 98% for Hybrid Workflow Architecture II.
Keywords: Parkinson disorder, transfer learning
DOI: 10.3233/JIFS-231202
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 769-787, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl